





'''simple docstring'''

import torch

from diffusers import UnCLIPScheduler

from .test_schedulers import SchedulerCommonTest
class 							__lowerCAmelCase	(       __magic_name__					):






       """simple docstring"""
       _snake_case      :	int									=       (UnCLIPScheduler,)





       def snake_case__		(				self  :						Dict			,					**lowerCAmelCase__  :						Any	)       ->	int:

            '''simple docstring'''




            _UpperCamelCase							       =			{
                '''num_train_timesteps''': 1000,
                '''variance_type''': '''fixed_small_log''',
                '''clip_sample''': True,
                '''clip_sample_range''': 1.0,
                '''prediction_type''': '''epsilon''',
            }

            config.update(**lowerCAmelCase__	)
            return config





       def snake_case__		(				self  :						str	)       ->	Optional[Any]:

            '''simple docstring'''




            for timesteps in [1, 5, 100, 1000]:
                 self.check_over_configs(num_train_timesteps=lowerCAmelCase__	)





       def snake_case__		(				self  :						int	)       ->	List[str]:

            '''simple docstring'''




            for variance in ["fixed_small_log", "learned_range"]:
                 self.check_over_configs(variance_type=lowerCAmelCase__	)





       def snake_case__		(				self  :						List[str]	)       ->	Union[str, Any]:

            '''simple docstring'''




            for clip_sample in [True, False]:
                 self.check_over_configs(clip_sample=lowerCAmelCase__	)





       def snake_case__		(				self  :						Any	)       ->	Union[str, Any]:

            '''simple docstring'''




            for clip_sample_range in [1, 5, 10, 20]:
                 self.check_over_configs(clip_sample_range=lowerCAmelCase__	)





       def snake_case__		(				self  :						List[Any]	)       ->	Any:

            '''simple docstring'''




            for prediction_type in ["epsilon", "sample"]:
                 self.check_over_configs(prediction_type=lowerCAmelCase__	)





       def snake_case__		(				self  :						List[Any]	)       ->	Optional[int]:

            '''simple docstring'''




            for time_step in [0, 500, 999]:
                 for prev_timestep in [None, 5, 100, 250, 500, 750]:
                      if prev_timestep is not None and prev_timestep >= time_step:
                           continue

                      self.check_over_forward(time_step=lowerCAmelCase__			,					prev_timestep=lowerCAmelCase__	)





       def snake_case__		(				self  :						List[str]	)       ->	int:

            '''simple docstring'''




            _UpperCamelCase							       =			self.scheduler_classes[0]
            _UpperCamelCase							       =			self.get_scheduler_config(variance_type='''fixed_small_log'''	)
            _UpperCamelCase							       =			scheduler_class(**lowerCAmelCase__	)

            assert torch.sum(torch.abs(scheduler._get_variance(0	) - 1.0_0_0_0e-1_0	)	) < 1e-5
            assert torch.sum(torch.abs(scheduler._get_variance(487	) - 0.0549625	)	) < 1e-5
            assert torch.sum(torch.abs(scheduler._get_variance(999	) - 0.9994987	)	) < 1e-5





       def snake_case__		(				self  :						List[str]	)       ->	Optional[int]:

            '''simple docstring'''




            _UpperCamelCase							       =			self.scheduler_classes[0]
            _UpperCamelCase							       =			self.get_scheduler_config(variance_type='''learned_range'''	)
            _UpperCamelCase							       =			scheduler_class(**lowerCAmelCase__	)

            _UpperCamelCase							       =			0.5

            assert scheduler._get_variance(1			,					predicted_variance=lowerCAmelCase__	) - -10.1712790 < 1e-5
            assert scheduler._get_variance(487			,					predicted_variance=lowerCAmelCase__	) - -5.7998052 < 1e-5
            assert scheduler._get_variance(999			,					predicted_variance=lowerCAmelCase__	) - -0.0010011 < 1e-5





       def snake_case__		(				self  :						Tuple	)       ->	int:

            '''simple docstring'''




            _UpperCamelCase							       =			self.scheduler_classes[0]
            _UpperCamelCase							       =			self.get_scheduler_config()
            _UpperCamelCase							       =			scheduler_class(**lowerCAmelCase__	)

            _UpperCamelCase							       =			scheduler.timesteps

            _UpperCamelCase							       =			self.dummy_model()
            _UpperCamelCase							       =			self.dummy_sample_deter
            _UpperCamelCase							       =			torch.manual_seed(0	)

            for i, t in enumerate(lowerCAmelCase__	):
                 # 1. predict noise residual
                 _UpperCamelCase							       =			model(lowerCAmelCase__			,					lowerCAmelCase__	)

                 # 2. predict previous mean of sample x_t-1
                 _UpperCamelCase							       =			scheduler.step(lowerCAmelCase__			,					lowerCAmelCase__			,					lowerCAmelCase__			,					generator=lowerCAmelCase__	).prev_sample

                 _UpperCamelCase							       =			pred_prev_sample

            _UpperCamelCase							       =			torch.sum(torch.abs(lowerCAmelCase__	)	)
            _UpperCamelCase							       =			torch.mean(torch.abs(lowerCAmelCase__	)	)

            assert abs(result_sum.item() - 252.2682495	) < 1e-2
            assert abs(result_mean.item() - 0.3284743	) < 1e-3





       def snake_case__		(				self  :						Optional[Any]	)       ->	Optional[Any]:

            '''simple docstring'''




            _UpperCamelCase							       =			self.scheduler_classes[0]
            _UpperCamelCase							       =			self.get_scheduler_config()
            _UpperCamelCase							       =			scheduler_class(**lowerCAmelCase__	)

            scheduler.set_timesteps(25	)

            _UpperCamelCase							       =			scheduler.timesteps

            _UpperCamelCase							       =			self.dummy_model()
            _UpperCamelCase							       =			self.dummy_sample_deter
            _UpperCamelCase							       =			torch.manual_seed(0	)

            for i, t in enumerate(lowerCAmelCase__	):
                 # 1. predict noise residual
                 _UpperCamelCase							       =			model(lowerCAmelCase__			,					lowerCAmelCase__	)

                 if i + 1 == timesteps.shape[0]:
                      _UpperCamelCase							       =			None
                 else:
                      _UpperCamelCase							       =			timesteps[i + 1]

                 # 2. predict previous mean of sample x_t-1
                 _UpperCamelCase							       =			scheduler.step(
                     lowerCAmelCase__			,					lowerCAmelCase__			,					lowerCAmelCase__			,					prev_timestep=lowerCAmelCase__			,					generator=lowerCAmelCase__	).prev_sample

                 _UpperCamelCase							       =			pred_prev_sample

            _UpperCamelCase							       =			torch.sum(torch.abs(lowerCAmelCase__	)	)
            _UpperCamelCase							       =			torch.mean(torch.abs(lowerCAmelCase__	)	)

            assert abs(result_sum.item() - 258.2044983	) < 1e-2
            assert abs(result_mean.item() - 0.3362038	) < 1e-3





       def snake_case__		(				self  :						Any	)       ->	Tuple:

            '''simple docstring'''




            pass







       def snake_case__		(				self  :						Optional[int]	)       ->	List[str]:

            '''simple docstring'''




            pass









'''simple docstring'''







from argparse import ArgumentParser

from ..pipelines import Pipeline, PipelineDataFormat, get_supported_tasks, pipeline
from ..utils import logging
from . import BaseTransformersCLICommand


lowerCAmelCase_     						=				logging.get_logger(__name__)  # pylint: disable=invalid-name







def 						_A      ( UpperCAmelCase						):






   '''simple docstring'''







   if not path:
      return "pipe"

   for ext in PipelineDataFormat.SUPPORTED_FORMATS:
      if path.endswith(UpperCAmelCase						):
         return ext

   raise Exception(
       F"""Unable to determine file format from file extension {path}. """
       F"""Please provide the format through --format {PipelineDataFormat.SUPPORTED_FORMATS}"""						)







def 						_A      ( UpperCAmelCase						):






   '''simple docstring'''







   A__       =     pipeline(
       task=args.task      ,model=args.model if args.model else None      ,config=args.config      ,tokenizer=args.tokenizer      ,device=args.device      ,)
   A__       =     try_infer_format_from_ext(args.input						) if args.format == 'infer' else args.format
   A__       =     PipelineDataFormat.from_str(
       format=UpperCAmelCase      ,output_path=args.output      ,input_path=args.input      ,column=args.column if args.column else nlp.default_input_names      ,overwrite=args.overwrite      ,)
   return RunCommand(UpperCAmelCase      ,UpperCAmelCase						)







class 	_snake_case(			UpperCAmelCase      ):






 def __init__(self       :  Tuple    ,		a       :  Pipeline    ,		a       :  PipelineDataFormat    )       ->   Union[str, Any]:






    """simple docstring"""


    A__       =     nlp
    A__       =     reader






 @staticmethod
 def 						_UpperCamelCase (a       :  ArgumentParser    )       ->   str:






    """simple docstring"""


    A__       =     parser.add_parser('run'    ,		help='Run a pipeline through the CLI'    )
    run_parser.add_argument('--task'    ,		choices=get_supported_tasks()    ,		help='Task to run'    )
    run_parser.add_argument('--input'    ,		type=a    ,		help='Path to the file to use for inference'    )
    run_parser.add_argument('--output'    ,		type=a    ,		help='Path to the file that will be used post to write results.'    )
    run_parser.add_argument('--model'    ,		type=a    ,		help='Name or path to the model to instantiate.'    )
    run_parser.add_argument('--config'    ,		type=a    ,		help='Name or path to the model\'s config to instantiate.'    )
    run_parser.add_argument(
        '--tokenizer'    ,		type=a    ,		help='Name of the tokenizer to use. (default: same as the model name)'    )
    run_parser.add_argument(
        '--column'    ,		type=a    ,		help='Name of the column to use as input. (For multi columns input as QA use column1,columns2)'    ,		)
    run_parser.add_argument(
        '--format'    ,		type=a    ,		default='infer'    ,		choices=PipelineDataFormat.SUPPORTED_FORMATS    ,		help='Input format to read from'    ,		)
    run_parser.add_argument(
        '--device'    ,		type=a    ,		default=-1    ,		help='Indicate the device to run onto, -1 indicates CPU, >= 0 indicates GPU (default: -1)'    ,		)
    run_parser.add_argument('--overwrite'    ,		action='store_true'    ,		help='Allow overwriting the output file.'    )
    run_parser.set_defaults(func=a    )






 def 						_UpperCamelCase (self       :  int    )       ->   Optional[Any]:






    """simple docstring"""


    A__  ,			A__       =     self._nlp, []

    for entry in self._reader:
       A__       =     nlp(**a    ) if self._reader.is_multi_columns else nlp(a    )
       if isinstance(a    ,		a    ):
          outputs.append(a    )
       else:
          outputs += output

        # Saving data
    if self._nlp.binary_output:
       A__       =     self._reader.save_binary(a    )
       logger.warning(f"""Current pipeline requires output to be in binary format, saving at {binary_path}"""    )
    else:
       self._reader.save(a    )


